kenya poverty data Uganda poverty data Tanzania poverty data Tanzania poverty data
library(plotly)
library(tidyverse)
library(data.table)
library(sf)
library(DT)
library(tmap)
library(ggthemes)poverty_ken <- fread("poverty_data/poverty_ken.csv")
poverty_uga <- fread("poverty_data/poverty_uga.csv")
poverty_tza <- fread("poverty_data/poverty_tza.csv")
poverty_rwa <- fread("poverty_data/poverty_rwa.csv")
poverty_ea <- rbind(poverty_ken, poverty_uga, poverty_rwa, poverty_tza)
poverty_ea[sample(nrow(poverty_ea), 10)] %>% datatable(options = list(scrollX = TRUE))## [1] "Country Name" "Country ISO3" "Year" "Indicator Name"
## [5] "Indicator Code" "Value"
## [1] "country_name" "country_iso3" "year" "indicator_name"
## [5] "indicator_code" "value"
setnames(poverty_ea, nms_old, nms_new)
poverty_ea[, value:= as.numeric(value)]
poverty_ea <- poverty_ea[!is.na(value)]
poverty_ea[sample(nrow(poverty_ea), 10)] %>% datatable(options = list(scrollX = TRUE))poverty_ea[, year := as.numeric(year)]
poverty_ea <- poverty_ea[!grepl("^Annu", indicator_name)]
poverty_ea_split <- split(poverty_ea, f = poverty_ea$indicator_name)
i = 1
n <- length(poverty_ea_split)
my_plots <-htmltools::tagList()
for (i in 1:n) {
df = poverty_ea_split[[i]]
my_title = df[, unique(indicator_name)]
mn = df[, min(year)]
mx = df[, max(year)]
breaks = seq(mn, mx,by = 2)
p = ggplot(df, aes(year, value, group = country_name, color = country_name) ) +
geom_line()+
theme_fivethirtyeight()+
labs(title = my_title, x = "year", y = "%")+
scale_color_viridis_d(name="")+
scale_x_continuous(breaks = breaks)
my_plots[[i]] = ggplotly(p)
}
my_plots